Where is evolution fastest? The biotic interactions hypothesis proposes that greater species richness creates more ecological opportunity, driving faster evolution at low latitudes, whereas the “empty niches” hypothesis proposes that ecological opportunity is greater where diversity is low, spurring faster evolution at high latitudes. We tested these contrasting predictions by analyzing rates of beak evolution for a global dataset of 1141 avian sister species. Rates of beak size evolution are similar across latitudes, with some evidence that beak shape evolves faster in the temperate zone, consistent with the empty niches hypothesis. The empty niches hypothesis is further supported by a meta-analysis showing that rates of trait evolution and recent speciation are generally faster in the temperate zone, whereas rates of molecular evolution are slightly faster in the tropics. Our results suggest that drivers of evolutionary diversification are either similar across latitudes or more potent in the temperate zone, thus calling into question multiple hypotheses that invoke faster tropical evolution to explain the latitudinal diversity gradient.

Dataset details:

1) sisterpairs.csv - file of sister pairs used in analysis, with information on range overlap and evolutionary age (from Cooney et al. 2017 Ecology Letters, "Sexual selection, speciation and constraints on geographical range overlap in birds"; please cite original publication if used)

2) rawdata.csv - file of morphological data; each row is an individual 

3) Morphdata.csv - file of morphological data; summarized as species' means

4) nonbreeding_range_overlaps.csv - file with range overlap data for non-breeding season

5) summarized_beak_data.csv (and summarized_beak_data_nonbreeding.csv) - datasets for analysis; each row a sister pair, includes bias-corrected estimates of beak size and beak shape divergence

6) metaanalysis.csv - information for studies used in meta-analysis

7) R scripts to (1) create summarized_beak_data datasets, including demonstration of how to calculate bias-correced estimates of divergence, (2) and (3) analyze beak size and beak shape divergence, (4) analyze overall patterns of trait divergence (Figure 3 in main text), and (5) analyze the meta-analysis, as well as additional scripts to do the same for resident/non-breeding dataset.